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Estimation of Mixture Models using Cross-Validation Optimization: Implications for Crop Yield Distribution Modeling

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  • Joshua D. Woodard
  • Bruce J. Sherrick

Abstract

A critical issue in identifying an appropriate characterization of crop yield distributions is that the best-fitting distribution in an in-sample framework is not necessarily the best choice out-of-sample. This study provides a methodology for estimating flexible and efficient mixture models using cross-validation that alleviates many of these associated model selection issues. The method is illustrated in an application to the rating of group risk insurance products. Results indicate that nonparametric models often fit best in-sample but are inefficient and consistently overstate true rates, and vice versa for parametric models. The proposed model provides unbiased rates and also has desirable efficiency properties. Copyright 2011, Oxford University Press.

Suggested Citation

  • Joshua D. Woodard & Bruce J. Sherrick, 2011. "Estimation of Mixture Models using Cross-Validation Optimization: Implications for Crop Yield Distribution Modeling," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(4), pages 968-982.
    • Handle: RePEc:oup:ajagec:v:93:y:2011:i:4:p:968-982
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    File URL: http://hdl.handle.net/10.1093/ajae/aar034
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    Citations

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    Cited by:

    1. Héctor M. Núñez & Andrés Trujillo-Barrera, 2015. "Impact of U.S. Biofuel Policy in the Presence of Drastic Climate Conditions," Working Papers DTE 585, CIDE, División de Economía.
    2. repec:ags:aaea22:335759 is not listed on IDEAS
    3. Liu, Y. & Ker, A., 2018. "Is There Too Much History in Historical Yield Data," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277293, International Association of Agricultural Economists.
    4. Joshua D. Woodard & Leslie J. Verteramo‐Chiu, 2017. "Efficiency Impacts of Utilizing Soil Data in the Pricing of the Federal Crop Insurance Program," American Journal of Agricultural Economics, John Wiley & Sons, vol. 99(3), pages 757-772, April.
    5. Woodard, Joshua D. & Chiu Verteramo, Leslie & Miller, Alyssa P., 2015. "Adaptation of U.S. Agricultural Production to Drought and Climate Change," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205903, Agricultural and Applied Economics Association.
    6. Ramirez, Octavio A. & Shonkwiler, J. Scott, 2017. "A Probabilistic Model of Crop Insurance Purchase Decision," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 42(1), pages 1-17, January.
    7. Hector, Nuñez & Andres, Trujillo-Barrera, 2014. "Impact of U.S. Biofuel Policy in the Presence of Uncertain Climate Conditions," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170620, Agricultural and Applied Economics Association.
    8. Woodard, Joshua & Wang, Diane & McClung, Anna & Ziska, Lewis & Dutta, Tridib & McCouch, Susan, 2016. "Integrating Variety Data into Large-Scale Crop Yield Models," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236170, Agricultural and Applied Economics Association.
    9. Jesse Tack & Keith Coble & Barry Barnett, 2018. "Warming temperatures will likely induce higher premium rates and government outlays for the U.S. crop insurance program," Agricultural Economics, International Association of Agricultural Economists, vol. 49(5), pages 635-647, September.
    10. Christopher N. Boyer & B. Wade Brorsen & Emmanuel Tumusiime, 2015. "Modeling skewness with the linear stochastic plateau model to determine optimal nitrogen rates," Agricultural Economics, International Association of Agricultural Economists, vol. 46(1), pages 1-10, January.

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